WO2018184419A1 - 一种精算处理方法和装置 - Google Patents
一种精算处理方法和装置 Download PDFInfo
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- WO2018184419A1 WO2018184419A1 PCT/CN2018/074863 CN2018074863W WO2018184419A1 WO 2018184419 A1 WO2018184419 A1 WO 2018184419A1 CN 2018074863 W CN2018074863 W CN 2018074863W WO 2018184419 A1 WO2018184419 A1 WO 2018184419A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
- G06F16/2433—Query languages
- G06F16/244—Grouping and aggregation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/06—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for block-wise or stream coding, e.g. DES systems or RC4; Hash functions; Pseudorandom sequence generators
- H04L9/0643—Hash functions, e.g. MD5, SHA, HMAC or f9 MAC
Definitions
- the present application relates to the field of financial services, and in particular, to an actuarial processing method and apparatus.
- the calculation of claims reserve is a very important part of risk management. Most insurance companies calculate the claim reserve every once in a while (such as once every half a month) to ensure that it happens. When claiming a case, you can complete the payment on time.
- the calculation of claims reserves is generally carried out through actuarial software, such as the actuarial program based on the PROPHET model.
- the embodiment of the present application provides an actuarial processing method and apparatus, which can reduce the workload of the actuarial program repeatedly processing the same data dimension value, and improve the efficiency of the actuarial processing.
- an actuarial processing method comprising:
- the target policy data is grouped according to a preset product grouping rule to obtain each data group;
- the actuarial processing of each of the groups of the actuarial data is performed separately by using a preset actuarial program.
- the embodiments of the present application have the following advantages:
- the target policy data having the same data dimension is divided into a group to be actuarial data according to the dimension mark, and the actuarial program is used to perform actuarial processing on the group to be actuarial data.
- the workload of the actuarial program repeatedly processing the same data dimension value is reduced, and the efficiency of the actuarial processing is improved; in the scenario of calculating the claim reserve, the time cost of the calculation is effectively reduced, and the calculation cost of the insurance company is saved.
- FIG. 1 is a flow chart of an embodiment of an actuarial processing method according to the present application.
- FIG. 2 is a schematic flowchart of an actuarial processing method step 104 in an application scenario according to the present application;
- FIG. 3 is a schematic flowchart of performing packet error processing in an application scenario by an actuarial processing method according to the present application
- FIG. 4 is a structural diagram of a first embodiment of an actuarial processing apparatus according to the present application.
- Figure 5 is a structural diagram of a second embodiment of an actuarial processing apparatus according to the present application.
- FIG. 6 is a structural diagram of a third embodiment of an actuarial processing apparatus according to the present application.
- an embodiment of an actuarial processing method of the present application includes:
- the data to be determined by the actuarial processing is different.
- the task of the actuarial processing is the actuarial calculation of the insurance company's claim reserve
- all the existing insurance policies of the insurance company can be determined as the target policy data to be actuated.
- the following content is mainly explained based on the actuarial processing of the claim reserve as an example. It should be understood that the actuarial processing method provided by the present application can also be applied to other actuarial tasks. This embodiment will not be described again.
- the target policy data is not located on the same server or In the database.
- the target policy data can be captured from multiple servers or databases of the insurance company by means of data statistics, and the target policy data is aggregated in a server or database to facilitate the actuarial processing of the subsequent actuarial program.
- model Point summary model point summary
- the basic data is prepared for the calculation of the claims reserve.
- a target policy data includes “type of insurance: life insurance, claim amount: 500W”, where “life insurance” is the value of the “insurance” attribute in the policy data, because it is not a number or character that is beneficial to the actuarial process.
- life insurance can be converted, and if "K001" is used instead, the data cleaning of the policy data "insurance” attribute is completed. It can be understood that the value of which data format is converted into data format when data cleaning is performed is generally determined by the actuarial program used in the subsequent steps.
- the product grouping rule can be set in advance, and when the target policy data is grouped, the product grouping rule is used to distinguish the target policy data generated by the insurance products with different data forms, and are divided into different data groups. In order to facilitate data dimension extraction and actuarial processing in subsequent steps.
- the above step 102 may include: grouping the target policy data according to the product name to which the target policy data belongs, to obtain each data group.
- each target policy data in the same data group belongs to the same or similar policy data of the insurance products, and the target policy data often has the same data dimension.
- each target policy data generally includes the amount of claims, premiums, various medical claims liabilities, insurance validity periods, additional risks, etc., and the values of these data dimensions are all the same or similar within a certain range, so These data dimensions can be extracted from this data set.
- a preset condition corresponding to each data group after grouping may be separately set to extract a data dimension of the corresponding data group.
- the data group is pre-set to which data dimensions need to be extracted as the "pre-condition" of the data group, and when extracting, the corresponding data dimension can be directly extracted from the target policy data of the data group.
- splicing processing may be performed on data values of the same data dimension, thereby generating a spliced string.
- splicing algorithms used to splicing data values, such as averaging, weighted averaging, summation, etc.
- different splicing algorithms may be preset for different data groups, specifically before step 104, respectively configuring corresponding data groups.
- the splicing algorithm, the splicing algorithms corresponding to the respective data groups are different from each other. It can be understood that, if different splicing algorithms are configured for different data groups, the possibility of the same splicing between the spliced strings is greatly reduced after the data dimensions of the data groups are extracted.
- the step of configuring the corresponding splicing algorithm for each of the data groups may include:
- the product name corresponding to the data group and the preset algorithm configuration table respectively configure a corresponding splicing algorithm for each of the data groups, and the algorithm configuration table records a correspondence between the product name and a preset splicing algorithm.
- the splicing algorithm may be acquired before the splicing process of the data values.
- the foregoing step 104 may include:
- the splicing algorithm corresponding to one of the acquired data sets is an averaging algorithm.
- the data dimension in the data group is “insurance period”, and the data values belonging to the “insurance period” dimension of the three target policy data of the data group are: 20130516-20180516 (ie, May 16 to 2018, 5, 2013) On the 16th of the month, the following values are similar, no longer explained), 20140213-20200213, 20160917-20220917, the average of these three data values, namely (20130516+20140213+20160917)/3-(20180516+20200213+20220917)/ 3, equal to 20143882-20200549 (rounded up).
- the spliced string is 20143882-20200549.
- the spliced string is encrypted into a 32-bit string by using the MD5 encryption mode, and the encrypted string is the dimension identifier corresponding to the data dimension, that is, the dimension ID.
- the target policy data in the data group can be further further grouped to obtain the individual data groups to be actuated. It can be seen that each target policy data in the same group of data to be actuated has the same dimension mark.
- the target policy data can be subjected to data cleaning processing. After the data is cleaned, the target policy data after the data cleaning process may be separately stored in a preset data storage path according to a preset storage requirement. Based on this, the foregoing step 106 may include:
- the service has different requirements for different policy data
- storing the target policy data after data cleaning to each data storage path is more convenient for the salesperson to query according to different needs.
- a path named "NB” only the new policy data generated this year is stored; on the path named "kaohe”, it is used to distinguish policy data from different databases.
- the data storage path is further added as a grouping basis, so that each group of the actuarial data to be obtained after the grouping is further subdivided, and the target policy data originally stored on different data storage paths is divided into an object to be actuated data group.
- the processing efficiency of the actuarial program is guaranteed to a certain extent.
- the foregoing step 106 may be: according to the dimension identifier corresponding to each of the data dimensions extracted in the data group, and the data storage path, the evaluation time point, and the insurance name of the target policy data, and the data group is The target policy data is grouped to obtain each of the data groups to be actuated under the data group.
- the evaluation point of the target policy data refers to the running time of the running AIO program (an agreed time).
- the name of the insurance policy of the target policy data refers to the name of the insurance policy of the policy data.
- different types of insurance can be modeled differently before the type of insurance is provided to the actuarial program.
- the actuarial processing may be performed on each of the groups of the actuarial data to be actuated by using a preset actuarial program, and the actuarial program may be prophet software or other actuarial software. This embodiment does not limit this.
- target policy data in each group to be actuarial data has data values of the same data dimension, it is not necessary to repeat the actuarial calculation of these data values when the actuarial program performs the actuarial processing on them.
- the actuarial processing method of this embodiment may further include:
- step 301 Determine, according to the log information, whether the data group or the to-be-prepared data group of the packet error exists, and if yes, execute step 302; if not, perform processing according to a preset process step;
- the above step 102 can be returned to the above, and the method of the embodiment is re-executed for packet processing and actuarial processing.
- the data accuracy of the actuarial task processing is guaranteed.
- the target policy data having the same data dimension is divided into a group to be actuarial data according to the dimension flag, and the actuarial program is used to perform actuarial calculation on the group to be actuarial data.
- the workload of the actuarial program repeatedly processing the same data dimension value is greatly reduced, and the efficiency of the actuarial processing is improved; in the scenario of calculating the claim reserve, the time cost of the calculation is effectively reduced, and the calculation of the insurance company is saved. cost.
- FIG. 4 is a structural diagram showing a first embodiment of an actuarial processing apparatus in an embodiment of the present application.
- an actuarial processing apparatus includes:
- the policy data determining module 401 is configured to determine target policy data to be actuarially processed
- the data grouping module 402 is configured to group the target policy data according to a preset product grouping rule to obtain each data group;
- a data dimension extraction module 403, configured to extract a data dimension in the data group that meets a preset condition
- the splicing module 404 is configured to perform splicing processing on data values belonging to the same data dimension in the data group to obtain a spliced string;
- the dimension flag module 405 is configured to perform encryption processing on the obtained spliced character string to obtain a dimension flag corresponding to the data dimension in the data group.
- the actuarial grouping module 406 is configured to group the target policy data under the data group according to the dimension identifier corresponding to each of the data dimensions extracted in the data group, to obtain each to be actuated under the data group.
- the actuarial processing module 407 is configured to perform an actuarial process on each of the to-be-amplified data groups by using a preset actuarial program.
- FIG. 5 is a structural diagram showing a second embodiment of an actuarial processing apparatus in an embodiment of the present application.
- the actuarial processing apparatus may further include:
- the algorithm configuration module 408 is configured to configure a corresponding splicing algorithm for each of the data groups, and the splicing algorithms corresponding to the data groups are different from each other;
- the splicing module 404 includes:
- An algorithm obtaining unit 4041 configured to acquire a splicing algorithm corresponding to the data group
- the splicing processing unit 4042 is configured to perform splicing processing on the data values belonging to the same data dimension in the data group according to the obtained splicing algorithm to obtain a spliced string.
- the data grouping module 402 can include:
- the policy data grouping unit 4021 is configured to group the target policy data according to the product name to which the target policy data belongs, to obtain each data group;
- the algorithm configuration module 408 includes:
- the splicing algorithm configuration unit 4081 is configured to configure a corresponding splicing algorithm for each of the data groups according to a product name corresponding to each of the data groups and a preset algorithm configuration table, where the algorithm configuration table records the product name and The correspondence between preset splicing algorithms.
- actuarial processing device may further include:
- the data cleaning module 409 is configured to perform data cleaning processing on the target policy data.
- the data storage module 410 is configured to store the target policy data after the data cleaning process according to a preset storage requirement to each preset data storage path;
- the to-be-actuate group grouping module 406 includes:
- the first grouping unit 4061 is configured to group the target policy data under the data group according to the dimension identifier corresponding to each of the data dimensions extracted in the data group, and each of the data storage paths. Each group of data to be actuated under the data group.
- FIG. 6 is a structural diagram showing a third embodiment of an actuarial processing apparatus in an embodiment of the present application.
- the actuarial group grouping module 406 can include:
- a second grouping unit 4062 configured to: according to the dimension identifier corresponding to each of the data dimensions extracted in the data group, and the data storage path, the evaluation time point, and the insurance name of the target policy data, to the data group
- the target policy data under the group is grouped, and each group of the data to be actuated under the data group is obtained.
- actuarial processing method may further include:
- a packet error judging module 411 configured to determine, according to the log information, whether the data group or the to-be-acquisition data group of the packet error exists;
- the return triggering module 412 is configured to return to trigger the data packet module 402 if the result of the determination by the group error determining unit is YES.
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Description
Claims (20)
- 一种精算处理方法,其特征在于,包括:确定待精算处理的目标保单数据;按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组;提取所述数据组中符合预设条件的数据维度;对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串;对得到的所述拼接字符串进行加密处理,得到所述数据组中与所述数据维度对应的维度标志;根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组;采用预设的精算程序分别对各个所述待精算数据小组进行精算处理。
- 根据权利要求1所述的精算处理方法,其特征在于,对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串之前,还包括:分别为各个所述数据组配置对应的拼接算法,各个所述数据组对应的拼接算法互不相同;所述对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串包括:获取所述数据组对应的拼接算法;根据获取到的所述拼接算法对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串。
- 根据权利要求2所述的精算处理方法,其特征在于,所述按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组包括:按照所述目标保单数据所属的产品名称对所述目标保单数据进行分组,得到各个数据组;所述分别为各个所述数据组配置对应的拼接算法包括:根据各个所述数据组对应的产品名称以及预设的算法配置表分别为各个所述数据组配置对应的拼接算法,所述算法配置表记录有所述产品名称与预设的拼接算法之间的对应关系。
- 根据权利要求1所述的精算处理方法,其特征在于,在确定待精算处理的目标保单数据之后,还包括:对所述目标保单数据进行数据清洗处理;将数据清洗处理后的所述目标保单数据按照预设的存放需求分别存放至预设的各个数据存放路径;所述根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组包括:根据所述数据组中提取到的各个所述数据维度对应的维度标志、以及各个所述数据存放路径对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组。
- 根据权利要求1所述的精算处理方法,其特征在于,所述根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组包括:根据所述数据组中提取到的各个所述数据维度对应的维度标志、以及所述目标保单数据的数据存放路径、评估时点和险种名称对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组。
- 根据权利要求1至5中任一项所述的精算处理方法,其特征在于,所述精算处理方法还包括:根据日志信息判断是否存在分组错误的所述数据组或者所述待精算数据小组;若存在分组错误的所述数据组或者所述待精算数据小组,则返回重新执行按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组的步骤。
- 一种精算处理装置,其特征在于,包括:保单数据确定模块,用于确定待精算处理的目标保单数据;数据分组模块,用于按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组;数据维度提取模块,用于提取所述数据组中符合预设条件的数据维度;拼接模块,用于对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串;维度标志模块,用于对得到的所述拼接字符串进行加密处理,得到所述数据组中与所述数据维度对应的维度标志;待精算小组分组模块,用于根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组;精算处理模块,用于采用预设的精算程序分别对各个所述待精算数据小组进行精算处理。
- 根据权利要求7所述的精算处理装置,其特征在于,所述精算处理装置还包括:算法配置模块,用于分别为各个所述数据组配置对应的拼接算法,各个所述数据组对应的拼接算法互不相同;所述拼接模块包括:算法获取单元,用于获取所述数据组对应的拼接算法;拼接处理单元,用于根据获取到的所述拼接算法对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串。
- 根据权利要求8所述的精算处理装置,其特征在于,所述数据分组模块包括:保单数据分组单元,用于按照所述目标保单数据所属的产品名称对所述目标保单数据进行分组,得到各个数据组;所述算法配置模块包括:拼接算法配置单元,用于根据各个所述数据组对应的产品名称以及预设的算法配置表分别为各个所述数据组配置对应的拼接算法,所述算法配置表记录有所述产品名称与预设的拼接算法之间的对应关系。
- 根据权利要求7至9中任一项所述的精算处理装置,其特征在于,所述精算处理装置还包括:数据清洗模块,用于对所述目标保单数据进行数据清洗处理;数据存放模块,用于将数据清洗处理后的所述目标保单数据按照预设的存放需求分别存放至预设的各个数据存放路径;所述待精算小组分组模块包括:第一小组分组单元,用于根据所述数据组中提取到的各个所述数据维度对应的维度标志、以及各个所述数据存放路径对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组。
- 一种终端设备,其特征在于,所述终端设备包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机可读指令,所述处理器执行所述计算机可读指令时实现如下步骤:确定待精算处理的目标保单数据;按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组;提取所述数据组中符合预设条件的数据维度;对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串;对得到的所述拼接字符串进行加密处理,得到所述数据组中与所述数据维度对应的维度标志;根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组;采用预设的精算程序分别对各个所述待精算数据小组进行精算处理。
- 根据权利要求11所述的终端设备,其特征在于,其特征在于,对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串之前,还包括:分别为各个所述数据组配置对应的拼接算法,各个所述数据组对应的拼接算法互不相同;所述对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串包括:获取所述数据组对应的拼接算法;根据获取到的所述拼接算法对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串。
- 根据权利要求12所述的终端设备,其特征在于,其特征在于,所述按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组包括:按照所述目标保单数据所属的产品名称对所述目标保单数据进行分组,得到各个数据组;所述分别为各个所述数据组配置对应的拼接算法包括:根据各个所述数据组对应的产品名称以及预设的算法配置表分别为各个所述数据组配置对应的拼接算法,所述算法配置表记录有所述产品名称与预设的拼接算法之间的对应关系。
- 根据权利要求11所述的终端设备,其特征在于,在确定待精算处理的目标保单数据之后,还包括:对所述目标保单数据进行数据清洗处理;将数据清洗处理后的所述目标保单数据按照预设的存放需求分别存放至预设的各个数据存放路径;所述根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组包括:根据所述数据组中提取到的各个所述数据维度对应的维度标志、以及各个所述数据存放路径对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组。
- 根据权利要求11-14任一项所述的终端设备,其特征在于,所述处理器执行所述计算机可读指令时还实现如下步骤:根据日志信息判断是否存在分组错误的所述数据组或者所述待精算数据小组;若存在分组错误的所述数据组或者所述待精算数据小组,则返回重新执行按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组的步骤。
- 一种计算机可读存储介质,所述计算机可读存储介质存储有计算机可读指令,其特征在于,所述计算机可读指令被处理器执行时实现如下步骤:确定待精算处理的目标保单数据;按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组;提取所述数据组中符合预设条件的数据维度;对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串;对得到的所述拼接字符串进行加密处理,得到所述数据组中与所述数据维度对应的维度标志;根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组;采用预设的精算程序分别对各个所述待精算数据小组进行精算处理。
- 根据权利要求16所述的计算机可读存储介质,其特征在于,对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串之前,还包括:分别为各个所述数据组配置对应的拼接算法,各个所述数据组对应的拼接算法互不相同;所述对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串包括:获取所述数据组对应的拼接算法;根据获取到的所述拼接算法对所述数据组中属于同一数据维度的数据值进行拼接处理,得到拼接字符串。
- 根据权利要求17所述的计算机可读存储介质,其特征在于,所述按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组包括:按照所述目标保单数据所属的产品名称对所述目标保单数据进行分组,得到各个数据组;所述分别为各个所述数据组配置对应的拼接算法包括:根据各个所述数据组对应的产品名称以及预设的算法配置表分别为各个所述数据组配置对应的拼接算法,所述算法配置表记录有所述产品名称与预设的拼接算法之间的对应关系。
- 根据权利要求16所述的计算机可读存储介质,其特征在于,在确定待精算处理的目标保单数据之后,还包括:对所述目标保单数据进行数据清洗处理;将数据清洗处理后的所述目标保单数据按照预设的存放需求分别存放至预设的各个数据存放路径;所述根据所述数据组中提取到的各个所述数据维度对应的维度标志对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组包括:根据所述数据组中提取到的各个所述数据维度对应的维度标志、以及各个所述数据存放路径对所述数据组下的目标保单数据进行分组,得到所述数据组下的各个待精算数据小组。
- 根据权利要求16-19任一项所述的计算机可读存储介质,其特征在于,所述计算机可读指令被处理器执行时还实现如下步骤:根据日志信息判断是否存在分组错误的所述数据组或者所述待精算数据小组;若存在分组错误的所述数据组或者所述待精算数据小组,则返回重新执行按照预设的产品分组规则对所述目标保单数据进行分组,得到各个数据组的步骤。
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